There has conventionally been a biometric identification technique which performs authentication by using human biometric information. In such a biometric identification technique, indices indicating authentication accuracies include a false rejection rate (FRR) and a false acceptance rate (FAR). The FRR is a probability that a person is incorrectly determined as another person, which is referred to as a person rejection rate. The FAR is a probability that another person is incorrectly accepted as a person, which is referred to as an other person acceptance rate.
It is preferable in the biometric identification technique that an FRR and an FAR be as smaller as possible. For this reason, there is such a technique including: extracting multiple feature quantities from one or more pieces of biometric information; and performing authentication by using the multiple feature quantities. For example, the multiple feature quantities include feature quantities which are used by matching of the feature point method and feature quantities which are used for matching of the pattern matching method from one piece of biometric information such as a fingerprint image of one finger.
A related technology is disclosed in Japanese Laid-open Patent Publication No. 2006-107340.
As a method which is used for authenticating multiple feature quantities, there is an approach called as a score level fusion, for example. In the score level fusion, feature quantities in registered data and matched data are matched for each of kinds of the feature quantities and the obtained multiple scores are fused, so that a final authentication result is obtained. As an example of the score level fusion, a discriminant function is obtained by machine learning such as a support vector machine (SVM) by using learning data including score distribution of each of the feature quantities which are classified into a person and another person in advance. For example, a distribution bias is caused in a genuine similarity when feature quantities extracted from biometric information of an identical person are compared with each other and impostor similarity when feature quantities extracted from biometric information of a different person are compared with each other. For this reason, a discriminant function which separates the distribution of the genuine similarity from the distribution of the impostor similarity is obtained by the machine learning. For example, in the SVM, a discriminant function is obtained that maximizes a margin of the distribution of the genuine similarity and the distribution of the impostor similarity.